Density Functional Theory and Machine Learning for Electrochemical Square-Scheme Prediction: An Application to Quinone-type Molecules Relevant to Redox Flow Batteries

Autor: Arsalan Hashemi, Reza Khakpour, Amir Mahdian, Michael Busch, Pekka Peljo, Kari Laasonen
Rok vydání: 2023
Předmět:
DOI: 10.5281/zenodo.7952776
Popis: The uploaded data contains (i) "01_Data"optimized molecular structure in XYZ format and theprimary attributes and SMILES, (ii)"02_Datasets" datasets used in the publication, and (iv) "03_pynb_script" a Jupyter-Notebook. The01_Data directory contains more than 8000 subdirectories. Each is for a molecule that undergoes a two-proton two-electron transfer reaction. In each subdirectory, one finds the following files: (1) directories named corresponding to the ones in Figure 1 of the paper. Inside each, there are geometries and properties in XYZ and CSV format, respectively. (2) "freeEnergy.dat"contains the free energy of different states. (3) "schemesquare.dat" hasthe parameters of the electrochemical scheme of square representation. ├── A │├── info.csv │└── pos.xyz ├── A1- │├── info.csv │└── pos.xyz ├── A2- │├── info.csv │└── pos.xyz ├── AH │├── info.csv │└── pos.xyz ├── AH1+ │├── info.csv │└── pos.xyz ├── AH1- │├── info.csv │└── pos.xyz ├── AH2 │├── info.csv │└── pos.xyz ├── AH21+ │├── info.csv │└── pos.xyz ├── AH22+ │├── info.csv │└── pos.xyz ├── freeEnergy.dat └── schemesquare.dat  
Databáze: OpenAIRE